Handle OMP_THREAD_LIMIT. (#7390)

tests/python/with_omp_limit.py

@@ -0,0 +1,26 @@
+import os
+import xgboost as xgb
+from sklearn.datasets import make_classification
+from sklearn.metrics import roc_auc_score
+import sys
+
+
+def run_omp(output_path: str):
+    X, y = make_classification(
+        n_samples=200, n_features=32, n_classes=3, n_informative=8
+    )
+    Xy = xgb.DMatrix(X, y, nthread=16)
+    booster = xgb.train(
+        {"num_class": 3, "objective": "multi:softprob", "n_jobs": 16},
+        Xy,
+        num_boost_round=8,
+    )
+    score = booster.predict(Xy)
+    auc = roc_auc_score(y, score, average="weighted", multi_class="ovr")
+    with open(output_path, "w") as fd:
+        fd.write(str(auc))
+
+
+if __name__ == "__main__":
+    out = sys.argv[1]
+    run_omp(out)